Author: jhoffma1

This week, my team gave final presentations, and we all made video clips for the 10 minute demo video.

In my video clips, I filmed the procedure of data collection. Dropping the dummy forward, backward, and collecting the data via walking.

This week, I collected data to improve the variety of our fall data set. Collecting a better variety of data can improve fall detection because SVM’s make decisions on margin vectors. Collecting more varieties of falls can have a significant impact on margin vectors. As well, I started work on the slides, demo video, and final paper with the rest of the group.

This week, I worked on integration of the SVM code with the Raspberry Pi with Sojeong and Max. I made sure to discuss with Max the expected input and output of the SVM code so it would be runnable on the Raspberry Pi, and then Sojeong and I made the necessary changes for Max.

As well, I began plotting dummy falls and human falls so I could see if there was any significant visual difference between the falls. From visual inspection, I could see no observable differences.

This week, our group demoed our current progress.

After the demo, I started work on integration of the machine learning algorithm with the Raspberry Pi, Max, Sojeong, and I started discussing how max would like the inputs and outputs of our code to be presented, so that when he runs our code on the Pi, his code can interface with our code.

This week, I collected data with the dummy. I collected 30 minutes of walking data, 30 minutes of falling data, and 30 minutes of sitting data. I then had to label the data. As well, I worked on plotting frequency features of falls. I noticed the frequency wavelet plots of fall segments had a specific appearance indicating these features will be beneficial.

This week, The test dummy arrived to my home. I commenced collecting fall data with the test dummy.

As well, I designed a system to adjust the parameters of the FFT and Wavelet frequency features which will be fed into the SVM, and also made a system to adjust the parameters of PCA compression that will happen during pre processing of features.

This week we made adjustments due to the COVID-19 Situation. The work we did this week can be found in the group posting.

This week, we looked at further design trade studies and decided to shift from focusing on RNN’s and SVM’s to working entirely on SVM’s due to power requirements of RNN’s on a Raspberry Pi chip.

Because the SVM design doesn’t have frequency domain features such as STFT and wavelet transform, I started working on feeding and tuning the paramaters of those features into the pipeline.

As well, SVM can sometimes benefit from PCA compression at the beginning of the SVM, so I started working on implementing and tuning the PCA compression.

This week, we decided a good way to collect preliminary data for designing our machine learning pipeline would be to use our smartphones. I took some falls in the hallway to collect some preliminary accelerometer data. I also began the design process for the RNN.

We also took our design phase discussions and fleshed them out into a visual presentation.